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Guanghao You; Moritz M. Daum; Sabine Stoll – Cognitive Science, 2024
Causation is a core feature of human cognition and language. How children learn about intricate causal meanings is yet unresolved. Here, we focus on how children learn verbs that express causation. Such verbs, known as lexical causatives (e.g., break and raise), lack explicit morphosyntactic markers indicating causation, thus requiring that the…
Descriptors: Language Acquisition, Verbs, Child Language, Adults
Jiang, Hang; Frank, Michael C.; Kulkarni, Vivek; Fourtassi, Abdellah – Cognitive Science, 2022
The linguistic input children receive across early childhood plays a crucial role in shaping their knowledge about the world. To study this input, researchers have begun applying distributional semantic models to large corpora of child-directed speech, extracting various patterns of word use/co-occurrence. Previous work using these models has not…
Descriptors: Caregivers, Caregiver Child Relationship, Linguistic Input, Semantics
Roete, Ingeborg; Frank, Stefan L.; Fikkert, Paula; Casillas, Marisa – Cognitive Science, 2020
We trained a computational model (the Chunk-Based Learner; CBL) on a longitudinal corpus of child-caregiver interactions in English to test whether one proposed statistical learning mechanism--backward transitional probability--is able to predict children's speech productions with stable accuracy throughout the first few years of development. We…
Descriptors: Statistics, Linguistic Input, Children, Speech Communication
Chang, Lucas M.; Deák, Gedeon O. – Cognitive Science, 2020
Children show a remarkable degree of consistency in learning some words earlier than others. What patterns of word usage predict variations among words in age of acquisition? We use distributional analysis of a naturalistic corpus of child-directed speech to create quantitative features representing natural variability in word contexts. We…
Descriptors: Vocabulary Development, Young Children, Child Language, Context Effect
Unger, Layla; Vales, Catarina; Fisher, Anna V. – Cognitive Science, 2020
The organization of our knowledge about the world into an interconnected network of concepts linked by relations profoundly impacts many facets of cognition, including attention, memory retrieval, reasoning, and learning. It is therefore crucial to understand how organized semantic representations are acquired. The present experiment investigated…
Descriptors: Semantics, Role, Schemata (Cognition), Language Processing
Rohde, Hannah; Frank, Michael C. – Cognitive Science, 2014
Although the language we encounter is typically embedded in rich discourse contexts, many existing models of processing focus largely on phenomena that occur sentence-internally. Similarly, most work on children's language learning does not consider how information can accumulate as a discourse progresses. Research in pragmatics, however,…
Descriptors: Caregiver Child Relationship, Discourse Analysis, Lexicology, Semantics
Theakston, Anna L.; Ibbotson, Paul; Freudenthal, Daniel; Lieven, Elena V. M.; Tomasello, Michael – Cognitive Science, 2015
Productivity is a central concept in the study of language and language acquisition. As a test case for exploring the notion of productivity, we focus on the noun slots of verb frames, such as __"want"__, __"see"__, and __"get"__. We develop a novel combination of measures designed to assess both the flexibility and…
Descriptors: Nouns, Verbs, Creativity, Semantics
Reali, Florencia; Christiansen, Morten H. – Cognitive Science, 2005
The poverty of stimulus argument is one of the most controversial arguments in the study of language acquisition. Here we follow previous approaches challenging the assumption of impoverished primary linguistic data, focusing on the specific problem of auxiliary (AUX) fronting in complex polar interrogatives. We develop a series of corpus analyses…
Descriptors: Language Acquisition, Grammar, Sentence Structure, Stimulus Generalization
Regier, Terry – Cognitive Science, 2005
Children improve at word learning during the 2nd year of life--sometimes dramatically. This fact has suggested a change in mechanism, from associative learning to a more referential form of learning. This article presents an associative exemplar-based model that accounts for the improvement without a change in mechanism. It provides a unified…
Descriptors: Associative Learning, Models, Semantics, Phonology